Geometries

Introduction to Data Visualization with ggplot2

Add a points layer to the base plot.

Set the point transparency to 0.5. Set shape = “.”, the point size of 1 pixel.

Update the point shape to remove the line outlines by setting shape to 16.

library(ggplot2)
library(dplyr)
library(forcats)

# Plot price vs. carat, colored by clarity
plt_price_vs_carat_by_clarity <- ggplot(diamonds, aes(carat, price, color = clarity))

# Add a point layer with tiny points
plt_price_vs_carat_by_clarity + geom_point(alpha = 0.5, shape = ".")

# Set transparency to 0.5
plt_price_vs_carat_by_clarity + geom_point(alpha = 0.5, shape = 16)

Create a base plot plt_mpg_vs_fcyl_by_fam of fcyl by mpg, colored by fam. Add a points layer to the base plot.

Add some jittering by using position_jitter(), setting the width to 0.3.

Alternatively, use position_jitterdodge(). Set jitter.width and dodge.width to 0.3 to separate subgroups further.

# add 'fcyl' column as cyl column converted to a factor &
# a 'fam' column as am column converted to a factor
mtcars <- mutate(mtcars, fcyl = as_factor(cyl), fam = as_factor(am))

# Plot base
plt_mpg_vs_fcyl_by_fam <- ggplot(mtcars, 
    aes(x = fcyl, y = mpg, color = fam))

# Default points are shown for comparison
plt_mpg_vs_fcyl_by_fam + geom_point()

# Alter the point positions by jittering, width 0.3
plt_mpg_vs_fcyl_by_fam + geom_point(position = position_jitter(width = 0.3))

# Now jitter and dodge the point positions
plt_mpg_vs_fcyl_by_fam + geom_point(position = position_jitterdodge(jitter.width = 0.3, dodge.width = 0.3))

Change the points layer into a jitter layer. Reduce the jitter layer’s width by setting the width argument to 0.1.

Let’s use a different approach:

Within geom_point(), set position to “jitter”.

Provide an alternative specification:

Have the position argument call position_jitter() with a width of 0.1.

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  # Swap for jitter layer with width 0.1
  geom_jitter(alpha = 0.5,
  width = 0.1)

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  # Set the position to jitter
  geom_point(position = "jitter", alpha = 0.5)

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  # Use a jitter position function with width 0.1
  geom_point(position = position_jitter(width = 0.1), alpha = 0.5)

Examine the Vocab dataset using str(). Using Vocab, draw a plot of vocabulary vs education. Add a point layer. Replace the point layer with a jitter layer.

library(carData) #load package that contains Vocab

# Examine the structure of Vocab
str(Vocab)

'data.frame':   30351 obs. of  4 variables:
 $ year      : num  1974 1974 1974 1974 1974 ...
 $ sex       : Factor w/ 2 levels "Female","Male": 2 2 1 1 1 2 2 2 1 1 ...
 $ education : num  14 16 10 10 12 16 17 10 12 11 ...
 $ vocabulary: num  9 9 9 5 8 8 9 5 3 5 ...
 - attr(*, "na.action")= 'omit' Named int  1 2 3 4 5 6 7 8 9 10 ...
  ..- attr(*, "names")= chr  "19720001" "19720002" "19720003" "19720004" ...

# Plot vocabulary vs. education
ggplot(Vocab, aes(x = education, y = vocabulary)) +
# Add a point layer  
geom_point()

ggplot(Vocab, aes(education, vocabulary)) +
  # Change to a jitter layer
  geom_jitter()

ggplot(Vocab, aes(education, vocabulary)) +
  # Set the transparency to 0.2
  geom_jitter(alpha = 0.2)

  
ggplot(Vocab, aes(education, vocabulary)) +
  # Set the shape to 1
  geom_jitter(alpha = 0.2, shape = 1)

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Using mtcars, map mpg onto the x aesthetic. Add a histogram layer using geom_histogram().

Set the histogram binwidth to 1.

# Plot mpg
ggplot(mtcars, aes(x = mpg)) +
  # Add a histogram layer
  geom_histogram()

ggplot(mtcars, aes(x = mpg)) +
  # Set the binwidth to 1
  geom_histogram(binwidth = 1)

# Map y to ..density..
ggplot(mtcars, aes(x= mpg, y = ..density..)) +
  geom_histogram(binwidth = 1)

datacamp_light_blue <- "#51A8C9"

ggplot(mtcars, aes(mpg, ..density..)) +
  # Set the fill color to datacamp_light_blue
  geom_histogram(binwidth = 1, fill = datacamp_light_blue)

Update the aesthetics so that the fill color of the bars is determined by fam.

Update the histogram layer to position the bars side-by-side, that is, “dodge”.

Update the histogram layer so the bars’ positions “fill” the y-axis.

Update the histogram layer so bars are top of each other, using the “identity” position. So each bar can be seen, set alpha to 0.4.

# Update the aesthetics so the fill color is by fam
ggplot(mtcars, aes(mpg, fill = fam)) +
  geom_histogram(binwidth = 1)

ggplot(mtcars, aes(mpg, fill = fam)) +
  # Change the position to dodge
  geom_histogram(binwidth = 1, position = "dodge")

ggplot(mtcars, aes(mpg, fill = fam)) +
  # Change the position to fill
  geom_histogram(binwidth = 1, position = "fill")

ggplot(mtcars, aes(mpg, fill = fam)) +
  # Change the position to identity, with transparency 0.4
  geom_histogram(alpha = 0.4, binwidth = 1, position = "identity")

Using mtcars, plot fcyl, filled by fam. Add a bar layer using geom_bar().

Set the bar position argument to “fill”.

Change the bar position argument to “dodge”.

# Plot fcyl, filled by fam
ggplot(mtcars, aes(x = fcyl, fill = fam)) +
  # Add a bar layer
  geom_bar()

ggplot(mtcars, aes(x = fcyl, fill = fam)) +
  # Set the position to "fill"
  geom_bar(position = "fill")

ggplot(mtcars, aes(fcyl, fill = fam)) +
  # Change the position to "dodge"
  geom_bar(position = "dodge")

Use the functional form of the bar position: replace “dodge” with a call to position_dodge(). Set its width to 0.2.

Set the bar transparency level of the bars to 0.6.

ggplot(mtcars, aes(cyl, fill = fam)) +
  # Change position to use the functional form, with width 0.2
  geom_bar(position = position_dodge(width = 0.2))

ggplot(mtcars, aes(cyl, fill = fam)) +
  # Set the transparency to 0.6
  geom_bar(alpha = 0.6, position = position_dodge(width = 0.2))

Plot the Vocab dataset, mapping education onto x and vocabulary onto fill.

Add a bar layer, setting position to “fill”.

Add a brewer fill scale, using the default palette (don’t pass any arguments). Notice how this generates a warning message and an incomplete plot.

# Plot education, filled by vocabulary
ggplot(Vocab, aes(x = education, fill = vocabulary))

# Plot education, filled by vocabulary
ggplot(Vocab, aes(x = education, fill = vocabulary)) +
  # Add a bar layer with position "fill"
  geom_bar(position = "fill")

# Plot education, filled by vocabulary
ggplot(Vocab, aes(x = education, fill = vocabulary)) +
  # Add a bar layer with position "fill"
  geom_bar(position = "fill") +
  # Add a brewer fill scale with default palette
    scale_fill_brewer()

Print the head of the economics dataset. Plot unemploy vs. date as a line plot.

Adjust the y-axis aesthetic of the plot so that it represents the proportion of the population that is unemployed.

# Print the head of economics
head(economics)

# Using economics, plot unemploy vs. date
ggplot(economics, aes(x = date, y = unemploy)) +
  # Make it a line plot
  geom_line()

# Change the y-axis to the proportion of the population that is unemployed
ggplot(economics, aes(x = date, y = unemploy/pop)) +
  geom_line()

Use str() in the console to examine the structure of both fish.species and fish.tidy. Plot only the Rainbow salmon time series with geom_line().

Plot only the Pink salmon time series with geom_line().

Now try and plot all time series in a single plot.

Plot the fish.tidy dataset, mapping Year to x and Capture to y. group by fish species within the aesthetics of geom_line().

Let’s add color to the previous plot to distinguish between the different time series.

Plot the fish.tidy dataset again, this time making sure to color by Species.

# Load fish.species file from csv, create tidyframe fish.tidy
fish.species <- read.csv("fish_species.csv")
fish.tidy <- gather(fish.species, Species, Capture, -Year)

# Plot the Rainbow Salmon time series
ggplot(fish.species, aes(x = Year, y = Rainbow)) +
  geom_line()

# Plot the Pink Salmon time series
ggplot(fish.species, aes(x = Year, y = Pink)) +
  geom_line()

# Plot multiple time-series by grouping by species
ggplot(fish.tidy, aes(x = Year, y = Capture)) +
  geom_line(aes(group = Species))

# Plot multiple time-series by coloring by species
ggplot(fish.tidy, aes(x = Year, y = Capture, color = Species)) +
  geom_line(aes(group = Species))